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Moving Toward a Strategy for Addressing Climate Displacement of Marine Resources: A Proof-of-Concept

Mangano, M. Cristina; Mieszkowska, Nova; Helmuth, Brian; Domingos, Tiago; Sousa, Tania; Baiamonte, Giuseppe; Bazan, Giuseppe; Cuttitta, Angela; Fiorentino, Fabio; Giacoletti, Antonio; Johnson, Magnus; Lucido, Giuseppe D.; Marcelli, Marco; Martellucci, Riccardo; Mirto, Simone; Patti, Bernardo; Pranovi, Fabio; Williams, Gray A.; Sarà, Gianluca

Authors

M. Cristina Mangano

Nova Mieszkowska

Brian Helmuth

Tiago Domingos

Tania Sousa

Giuseppe Baiamonte

Giuseppe Bazan

Angela Cuttitta

Fabio Fiorentino

Antonio Giacoletti

Giuseppe D. Lucido

Marco Marcelli

Riccardo Martellucci

Simone Mirto

Bernardo Patti

Fabio Pranovi

Gray A. Williams

Gianluca Sarà



Abstract

Realistic predictions of climate change effects on natural resources are central to adaptation policies that try to reduce these impacts. However, most current forecasting approaches do not incorporate species-specific, process-based biological information, which limits their ability to inform actionable strategies. Mechanistic approaches, incorporating quantitative information on functional traits, can potentially predict species- and population-specific responses that result from the cumulative impacts of small-scale processes acting at the organismal level, and can be used to infer population-level dynamics and inform natural resources management. Here we present a proof-of-concept study using the European anchovy as a model species that shows how a trait-based, mechanistic species distribution model can be used to explore the vulnerability of marine species to environmental changes, producing quantitative outputs useful for informing fisheries management. We crossed scenarios of temperature and food to generate quantitative maps of selected mechanistic model outcomes (e.g., Maximum Length and Total Reproductive Output). These results highlight changing patterns of source and sink spawning areas as well as the incidence of reproductive failure. This study demonstrates that model predictions based on functional traits can reduce the degree of uncertainty when forecasting future trends of fish stocks. However, to be effective they must be based on high spatial- and temporal resolution environmental data. Such a sensitive and spatially explicit predictive approach may be used to inform more effective adaptive management strategies of resources in novel climatic conditions.

Citation

Mangano, M. C., Mieszkowska, N., Helmuth, B., Domingos, T., Sousa, T., Baiamonte, G., …Sarà, G. (2020). Moving Toward a Strategy for Addressing Climate Displacement of Marine Resources: A Proof-of-Concept. Frontiers in Marine Science, 7, Article 408. https://doi.org/10.3389/fmars.2020.00408

Journal Article Type Article
Acceptance Date May 12, 2020
Online Publication Date Jul 2, 2020
Publication Date Jul 2, 2020
Deposit Date Apr 1, 2022
Publicly Available Date Apr 6, 2022
Journal Frontiers in Marine Science
Electronic ISSN 2296-7745
Publisher Frontiers Media
Peer Reviewed Peer Reviewed
Volume 7
Article Number 408
DOI https://doi.org/10.3389/fmars.2020.00408
Keywords Climate-informed management; Dynamic Energy Budget model; Engraulis encrasicolus; Life-history traits; Scenarios; Temperature increase
Public URL https://hull-repository.worktribe.com/output/3532765

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0

Copyright Statement
Copyright © 2020 Mangano, Mieszkowska, Helmuth, Domingos, Sousa, Baiamonte, Bazan, Cuttitta, Fiorentino, Giacoletti, Johnson, Lucido, Marcelli, Martellucci, Mirto, Patti, Pranovi, Williams and Sarà. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.





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